Predictive machine learning models for ascending aortic dilatation in patients with bicuspid and tricuspid aortic valves undergoing cardiothoracic surgery: a prospective, single-centre and observational study.
Bamba GayeMaxime VignacJesper R GådinMagalie LadouceurKenneth CaidahlChristian OlssonAnders Franco-CerecedaPer ErikssonHanna M BjörckPublished in: BMJ open (2024)
Cardiovascular risk profiles appear to be more predictive of aortopathy in TAV patients than in patients with BAV. This adds evidence to the fact that BAV-associated and TAV-associated aortopathy involves different pathways to aneurysm formation and highlights the need for specific aneurysm preventions in these patients. Further, our results highlight that machine learning approaches do not outperform classical prediction methods in addressing complex interactions and non-linear relations between variables.
Keyphrases
- aortic valve
- machine learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- coronary artery
- aortic stenosis
- minimally invasive
- pulmonary artery
- prognostic factors
- transcatheter aortic valve replacement
- artificial intelligence
- patient reported outcomes
- transcatheter aortic valve implantation
- aortic valve replacement
- mitral valve
- big data
- aortic dissection
- pulmonary arterial hypertension
- deep learning
- coronary artery disease
- abdominal aortic aneurysm
- patient reported